Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)最新文献

Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction 从关联锚图学习子句表示用于连接预测

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.6

Yanjun Gao, Ting-Hao 'Kenneth' Huang, R. Passonneau

{"title":"Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction","authors":"Yanjun Gao, Ting-Hao 'Kenneth' Huang, R. Passonneau","doi":"10.18653/V1/11.TEXTGRAPHS-1.6","DOIUrl":"https://doi.org/10.18653/V1/11.TEXTGRAPHS-1.6","url":null,"abstract":"Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing. We propose a novel clause embedding method that applies graph learning to a data structure we refer to as a dependency-anchor graph. The dependency anchor graph incorporates two kinds of syntactic information, constituency structure, and dependency relations, to highlight the subject and verb phrase relation. This enhances coherence-related aspects of representation. We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase. We evaluate our method on two new datasets: a subset of a large corpus where the source texts are published novels, and a new dataset collected from students’ essays. The results demonstrate a significant improvement over tree-based models, confirming the importance of emphasizing the subject and verb phrase. The performance gap between the two datasets illustrates the challenges of analyzing student’s written text, plus a potential evaluation task for coherence modeling and an application for suggesting revisions to students.","PeriodicalId":332938,"journal":{"name":"Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115981067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Three-step Method for Multi-Hop Inference Explanation Regeneration 多跳推理解释再生的三步法

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.19

Yuejia Xiang, Yunyan Zhang, Xiaoming Shi, Bo Liu, Wandi Xu, Xi Chen

引用次数: 1

Improving Human Text Simplification with Sentence Fusion 用句子融合改进人类文本简化

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.10

Max Schwarzer, Teerapaun Tanprasert, David Kauchak

引用次数: 2

Entity Prediction in Knowledge Graphs with Joint Embeddings 基于联合嵌入的知识图实体预测

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.3

Matthias Baumgartner, Daniele Dell'Aglio, A. Bernstein

{"title":"Entity Prediction in Knowledge Graphs with Joint Embeddings","authors":"Matthias Baumgartner, Daniele Dell'Aglio, A. Bernstein","doi":"10.18653/V1/11.TEXTGRAPHS-1.3","DOIUrl":"https://doi.org/10.18653/V1/11.TEXTGRAPHS-1.3","url":null,"abstract":"Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.","PeriodicalId":332938,"journal":{"name":"Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133964887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration TextGraphs 2021多跳推理解释再生共享任务

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.17

Peter Alexander Jansen, Mokanarangan Thayaparan, Marco Valentino, Dmitry Ustalov

{"title":"TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration","authors":"Peter Alexander Jansen, Mokanarangan Thayaparan, Marco Valentino, Dmitry Ustalov","doi":"10.18653/V1/11.TEXTGRAPHS-1.17","DOIUrl":"https://doi.org/10.18653/V1/11.TEXTGRAPHS-1.17","url":null,"abstract":"The Shared Task on Multi-Hop Inference for Explanation Regeneration asks participants to compose large multi-hop explanations to questions by assembling large chains of facts from a supporting knowledge base. While previous editions of this shared task aimed to evaluate explanatory completeness – finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations. To this end, this edition of the shared task makes use of a large set of approximately 250k manual explanatory relevancy ratings that augment the 2020 shared task data. In this summary paper, we describe the details of the explanation regeneration task, the evaluation data, and the participating systems. Additionally, we perform a detailed analysis of participating systems, evaluating various aspects involved in the multi-hop inference process. The best performing system achieved an NDCG of 0.82 on this challenging task, substantially increasing performance over baseline methods by 32%, while also leaving significant room for future improvement.","PeriodicalId":332938,"journal":{"name":"Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Structural Realization with GGNNs ggnn的结构实现

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.11

Jinman Zhao, Gerald Penn, Huan Ling

引用次数: 0

Hierarchical Graph Convolutional Networks for Jointly Resolving Cross-document Coreference of Entity and Event Mentions 分层图卷积网络联合解决实体和事件提及的跨文档共同引用

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.4

Duy Phung, Tuan Ngo Nguyen, Thien Huu Nguyen

引用次数: 8

Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings Textgraphs-15共享任务系统描述:匹配专家评级的多跳推理解释再生

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.20

Sureshkumar Vivek Kalyan, Sam Witteveen, Martin Andrews

引用次数: 1

MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling 用于屏蔽语言建模的多图增强BERT

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.12

Parishad BehnamGhader, Hossein Zakerinia, Mahdieh Soleymani Baghshah

引用次数: 1

Keyword Extraction Using Unsupervised Learning on the Document’s Adjacency Matrix 基于文档邻接矩阵的无监督学习关键字提取

Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) Pub Date : 2021-06-01 DOI: 10.18653/V1/11.TEXTGRAPHS-1.9

Eirini Papagiannopoulou, Grigorios Tsoumakas, A. Papadopoulos

引用次数: 6